The Boost C++ Metaprogramming Library

Aleksey Gurtovoyi and David Abrahamsii

This paper describes the Boost C++ template metaprogramming library (MPL), an extensible compile-time framework of algorithms, sequences and metafunction classes. The library brings together important abstractions from the generic and functional programming worlds to build a powerful and easy-to-use toolset which makes template metaprogramming practical enough for the real-world environments. The MPL is heavily influenced by its run-time equivalent - the Standard Template Library (STL), a part of the C++ standard library [STL94], [ISO98]. Like the STL, it defines an open conceptual and implementation framework which can serve as a foundation for future contributions in the domain. The library's fundamental concepts and idioms enable the user to focus on solutions without navigating the universe of possible ad-hoc approaches to a given metaprogramming problem, even if no actual MPL code is used. The library also provides a compile-time lambda expression facility enabling arbitrary currying and composition of class templates, a feature whose runtime counterpart is often cited as missing from the STL. This paper explains the motivation, usage, design, and implementation of the MPL with examples of its real-life applications, and offers some lessons learned about C++ template metaprogramming.

1. Introduction

Metaprogramming is usually defined as the creation of programs which generate other programs. Parser generators such as YACC [Joh79] are examples of one kind of program-generating program. The input language to YACC is a context-free grammar in Extended Backus-Naur Form [EBNF], and its output is a program which parses that grammar. Note that in this case the metaprogram (YACC) is written in a language (C) which does not directly support the description of generated programs. These specifications, which we'll call metadata, are not written in C, but in a meta-language. Because the the rest of the user's program typically requires a general-purpose programming system and must interact with the generated parser, the metadata is translated into C, which is then compiled and linked together with the rest of the system. The metadata thus undergoes two translation steps, and the user is always very conscious of the boundary between her metadata and the rest of her program.

1.1. Native language metaprogramming

A more interesting form of metaprogramming is available in languages such as Scheme [SS75], where the generated program specification is given in the same language as the metaprogram itself. The metaprogrammer defines her meta-language as a subset of the expressible forms of the underlying language, and program generation can take place in the same translation step used to process the rest of the user's program. This allows users to switch transparently between ordinary programming, generated program specification, and metaprogramming, often without being aware of the transition.

1.2. Metaprogramming in C++

In C++, it was discovered almost by accident [Unr], [Vel95a] that the template mechanism provides a rich facility for computation at compile-time. In this section, we'll explore the basic mechanisms and some common idioms used for metaprogramming in C++.

1.2.1. Numeric computations

The availability of non-type template parameters makes it possible to perform integer computations at compile-time. For example, the following template computes the factorial of its argument:

The program fragment above is called a metafunction, and it is easy to see its relationship to a function designed to be evaluated at runtime: the “metafunction argument” is passed as a template parameter, and its “return value” is defined as a nested static constant. Because of the hard line between the expression of compile-time and runtime computation in C++, metaprograms look different from their runtime counterparts. Thus, although as in Scheme the C++ metaprogrammer writes her code in the same language as the ordinary program, only a subset of the full C++ language is available to her: those expressions which can be evaluated at compile-time. Compare the above with a straightforward runtime definition of the factorial function:

While it is easy to see the analogy between the two recursive definitions, recursion is in general more important to C++ metaprograms than it is to runtime C++. In contrast to languages such as Lisp where recursion is idiomatic, C++ programmers will typically avoid recursion when possible. This is done not only for efficiency reasons, but also because of “cultural momentum”: recursive programs are simply harder (for C++ programmers) to think about. Like pure Lisp, though, the C++ template mechanism is a functional programming language: as such it rules out the use of data mutation required to maintain loop variables.

A key difference between the runtime and compile-time factorial functions is the expression of the termination condition: our meta-factorial uses template specialization as a kind of pattern-matching mechanism to describe the behavior when N is zero. The syntactic analogue in the runtime world would require two separate definitions of the same function. In this case the impact of the second definition is minimal, but in large metaprograms the cost of maintaining and understanding the terminating definitions can become significant.

Note also that a C++ metafunction's return value must be named. The name chosen here, value, is the same one used for all numeric returns in the MPL. As we'll see, establishing a consistent naming convention for metafunction returns is crucial to the power of the library.

1.2.2. Type computations

How could we apply our factorial metafunction? We might, for example, produce an array type of an appropriate size to hold all permutations of instances of another type:

Here we have introduced the notion of a type computation. Like factorial above, permutation_holder template is a metafunction. However, where factorial manipulates unsigned integer values, permutation_holder accepts and “returns” a type (as the nested typedef type). Because the C++ type system provides a much richer set of expressions than anything we can use as a nontype template argument (e.g. the integers), C++ metaprograms tend to be composed mostly of type computations.

1.2.3. Type sequences

The ability to programmatically manipulate collections of types is a central tool of most interesting C++ metaprograms. Because this capability is so well-supported by the MPL, we'll provide just a brief introduction to the basics here. Later on, we'll revisit the example below to show how it can be implemented using MPL.

First, we'd need a way to represent the collection. One idea might be to store the types in a structure:

struct types
{
int t1;
long t2;
std::vector<double> t3;
};

Unfortunately, this arrangement is not susceptible to the compile-time type introspection power that C++ gives us: there's no way to find out what the names of the members are, and even if we assume that they're named according to some convention as above, there's no way to know how many members there are. The key to solving this problem is to increase the uniformity of the representation. If we have a consistent way to get the first type of any sequence and the rest of the sequence, we can easily access all members:

The structure described by types above is the compile-time analogue of a singly-linked list; it has been first introduced by Czarnecki and Eisenecker in [CE98]. Now that we've adjusted the structure so that the C++ template machinery can “peel it apart”, let's examine a simple metafunction which does so. Suppose a user wished to find the largest of an arbitrary collection of types. We can apply the recursive metafunction formula which should by now be familiar:

It uses a few ad-hoc, esoteric techniques, or “hacks”. The default template argument choose1 (labeled “hands off!”) is one example. Without it, we would have needed yet another template to provide the implementation of choose_larger, or we would have had to provide the computation explicitly as a parameter to the template - perhaps not bad for this example, but it would make choose_larger much less useful and more error-prone. The other hack is the derivation of a specialization of largest from choose_larger. This is a code-saving device which allows the programmer to avoid writing “typedef typename ...::type type” in the template body.

Even this simple metaprogram uses three separate partial specializations. The largest metafunction uses two specializations. One might expect that this indicates there are two termination conditions, but there are not: one specialization is needed simply to deal with access to the sequence elements. These specializations make the code difficult to read by spreading the definition of a single metafunction over several C++ template definitions. Also, because they are partial specializations, they make the code unusable for a large community of C++ programmers whose compilers don't support that feature.

While these techniques are, of course, a valuable part of the arsenal of any good C++ metaprogrammer, their use tends to make programs written in what is already an unusual style harder-to-read and harder-to-write. By encapsulating commonly-used structures and dealing with loop terminations internally, the MPL reduces the need for both tricky hacks and for template specializations.

1.3. Why metaprogramming?

It's worth asking why anyone would want to do this. After all, even a simple toy example like the factorial metafunction is somewhat esoteric. To show how the type computation can be put to work, let's examine a simple example. The following code produces an array containing all possible permutations of another array:

The runtime definition of factorial would be useless in all_permutations above, since in C++ the sizes of array members must be computed at compile-time. However, there are alternative approaches; how could we avoid metaprogramming, and what would the consequences be?

We could write programs to interpret the metadata directly. In our factorial example, the array size could have been a runtime quantity; then we'd have been able to use the straightforward factorial function. However, that would imply the use of dynamic allocation, which is often expensive.

To carry this further, YACC might be rewritten to accept a pointer-to-function returning tokens from the stream to be parsed, and a string containing the grammar description. This approach, however, would impose unacceptable runtime costs for most applications: either the parser would have to treat the grammar nondeterministically, exploring the grammar for each parse, or it would have to begin by replicating at runtime the substantial table-generation and optimization work of the existing YACC for each input grammar.

We could replace the compile-time computation with our own analysis. After all, the size of arrays passed to all_permutations are always known at compile-time, and thus can be known to its user. We could ask the user to supply the result type explicitly:

The costs to this approach are obvious: we give up expressivity (by requiring the user to explicitly specify implementation details), and correctness (by allowing the user to specify them incorrectly). Anyone who has had to write parser tables by hand will tell you that the impracticality of this approach is the very reason of YACC's existence.

In a language such as C++, where the metadata can be expressed in the same language as the rest of the user's program, expressivity is further enhanced: the user can invoke metaprograms directly, without learning a foreign syntax or interrupting the flow of her code.

So, the motivation for metaprogramming comes down to the combination of three factors: efficiency, expressivity, and correctness. While in classical programming there is always a tension between expressivity and correctness on one hand and efficiency on the other, in the metaprogramming world we wield new power: we can move the computation required for expressivity from runtime to compile-time.

1.4. Why a metaprogramming library?

One might just as well ask why we need any generic library:

Quality. Code that is appropriate for a general-purpose library is usually incidental to the purpose of its users. To a library developer, it is the central mission. On average, the containers and algorithms provided by any given C++ standard library implementation are more-flexible and better-implemented than the project-specific implementations which abound, because library development was treated as an end in itself rather than a task incidental to the development of some other application. With a centralized implementation for any given function, optimizations and improvements are more likely to have been applied.

Re-use. More important even than the re-use of code which all libraries provide, a well-designed generic library establishes a framework of concepts and idioms which establishes a reusable mental model for approaching problems. Just as the C++ Standard Template Library gave us iterator concepts and a function object protocol, the Boost Metaprogramming Library provides type-iterators and metafunction class protocol. A well-considered framework of idioms saves the metaprogrammer from considering irrelevant implementation details and allows her to concentrate on the problem at hand.

Portability. A good library can smooth over the ugly realities of platform differences. While in theory a metaprogramming library is fully generic and shouldn't be concerned with these issues, in practice support for templates remains inconsistent even four years after standardization. This should perhaps not be surprising: C++ templates are the language's furthest-reaching and most complicated feature, which largely accounts for the power of metaprogramming in C++.

Fun. Repeating the same idioms over and over is tedious. It makes programmers tired and reduces productivity. Furthermore, when programmers get bored they get sloppy, and buggy code is even more costly than slowly-written code. Often the most useful libraries are simply patterns that have been “plucked” by an astute programmer from a sea of repetition. The MPL helps to reduce boredom by eliminating the need for the most commonly-repeated boilerplate coding patterns.

As one can see, the MPL's development is motivated primarily by the same practical, real-world considerations that justify the development of any other library. Perhaps this is an indication that template metaprogramming is finally ready to leave the realm of the esoteric and enter the lingua franca of every day programmers.

2. Basic usage

2.1. Conditional type selection

Conditional type selection is the simplest basic construct of C++ template metaprogramming. Veldhuizen [Vel95a] was the first to show how to implement it, and Czarnecki and Eisenecker [CE00] first presented it as a standalone library primitive. The MPL defines the corresponding facility as follows:

The construct is important because template metaprograms often contain a lot of decision-making code, and, as we will show, spelling it manually every time via (partial) class template specialization quickly becomes impractical. The template is also important from the point of encapsulating the compiler workarounds.

2.1.1. Delayed evaluation

The way the C++ template instantiation mechanism works imposes some subtle limitations on applicability of the type selection primitive (if_), compared to a manually implemented equivalent of the selection code. For example, suppose we are implementing a pointed_type traits template such that pointed_type<T>::type instantiated for a T that is either a plain pointer (U*), std::auto_ptr<U>, or any of the Boost smart pointers [SPL], e.g. boost::scoped_ptr<U>, will give us the pointed type (U):

but this doesn't work either - the access to the aux::element_type<T>'s nested type member still forces the compiler to instantiate element_type<T> with T == char*, and that instantiation is, of course, invalid. Also, although in our case this does not lead to a compile error, the boost::remove_pointer<T> template always gets instantiated as well, and for the same reason (because we are accessing its nested type member). Unnecessary instantiation that is not fatal may or may be not a problem, depending on the “weight” of the template (how much the instantiation taxes the compiler), but a general rule of thumb would be to avoid such code.

Returning to our error, to make the above code compile, we need to factor the act of “asking” aux::element_type<T> for its nested type out of the if_ invocation. The fact that both the boost::remove_pointer<T> trait template and aux::element_type<T> use the same naming convention for their result types makes the refactoring easier:

Now the compiler is guaranteed not to instantiate both boost::remove_pointer<T> and aux::element_type<T>, even although they are used as actual parameters to the if_ template, so we are allowed to get away with aux::element_type<char*> so long as it won't end up being selected as func_.

The above technique is so common in template metaprograms, that it even makes sense to facilitate the selection of a nested type member by introducing a high-level equivalent to if_ - the one that will do the func_::type operation (that is called [nullary] metafunction class application) as a part of its invocation. The MPL provides such template - it's called apply_if. Using it, we can re-write the above code as simple as:

To make our techniques review complete, let's consider a slightly different example - suppose we want to define a high-level wrapper around boost::remove_pointer traits template [TTL], which will strip the pointer qualification conditionally. We will call it remove_pointer_if:

Now the above works the first time, but it suffers from the problem we mentioned earlier - boost::remove_pointer<T> gets instantiated even if its result is never used. In the metaprogramming world compilation time is an important resource [Abr01], and it is wasted by unnecessary template instantiations. We've just seen how to deal with the problem when both arguments to if_ are the results of nullary metafunction class applications, but in this example one of the arguments (T) is just a simple type, so the refactoring just doesn't seem possible.

The easiest way out of this situation would be to pass to if_ a real nullary metafunction instead of T - the one that returns T on its invocation. The MPL provides a simple way to do it - we just substitute identity<T> and apply_if for T and if_:

2.2. Metafunctions

2.2.1. The simple form

In C++, the basic underlying language construct which allows parameterized compile-time computation is the class template ([ISO98], section 14.5.1 [temp.class]). A bare class template is the simplest possible model we could choose for metafunctions: it can take types and/or non-type arguments as actual template parameters, and instantiation “returns” a new type. For example, the following produces a type derived from its arguments:

template< typename T1, typename T2 >
struct derive : T1, T2
{
};

However, this model is far too limiting: it restricts the metafunction result not only to class types, but to instantiations of a given class template, to say nothing of the fact that every metafunction invocation introduces an additional level of template nesting. While that might be acceptable for this particular metafunction, any model which prevented us from “returning”, say, int is obviously not general enough. To meet this basic requirement, we must rely on a nested type to provide our return value:

Veldhuizen [Vel95a] was first to talk about class templates of this form as “compile-time functions”, and Czarnecki and Eisenecker [CE00] have introduced “template metafunction” as an equivalent term (they also use the simpler term “metafunction”, as do we). Czarnecki and Eisenecker have also recognized the limitations of the simple metafunction representation and suggested the form that we discuss in Section 2.2.3.

2.2.2. Higher-order metafunctions

While syntactically simple, the simple template metafunction form does not always interact optimally with the rest of C++. In particular, the simple metafunction form makes it unnecessarily awkward and tedious to define and work with higher-order metafunctions (metafunctions that operate on other metafunctions). In order to pass a simple metafunction to another template, we need to use template template parameters:

The first and most obvious problem is that the result of applying compose_self is not itself a type, but a template, so it can't be passed in the usual ways to other metafunctions. A more subtle issue, however, is that the metafunction “returned” is not exactly what we intended. Although it acts just like apply_twice, it differs in one important respect: its identity. In the C++ type system, compose_self<F>::template type<T,U,V> is not a synonym for apply_twice<F,T,U,V>, and any metaprogram which compared metafunctions would discover that fact.

Because C++ makes a strict distinction between type and class template template parameters, reliance on simple metafunctions creates a “wall” between metafunctions and metadata, relegating metafunctions to the status of second-class citizens. For example, recalling our introduction to type sequences, there's no way to make a cons list of metafunctions:

typedef cons<derive, cons<derive3, nil> > derive_functions; // error!

We might consider redefining our cons cell so we can pass derive as the head element:

However, now we have another problem: C++ templates are polymorphic with respect to their type arguments, but not with respect to template template parameters. The arity (number of parameters) of any template template parameter is strictly enforced, so we still can't embed derive3 in a cons list. Moreover, polymorphism between types and metafunctions is not supported (the compiler expects one or the other), and as we've seen, the syntax and semantics of “returned” metafunctions is different from that of returned types. Trying to accomplish everything with the simple template metafunction form would seriously limit the applicability of higher-order metafunctions and would have an overall negative effect on the both conceptual and implementation clarity, simplicity and size of the library.

2.2.3. Metafunction classes

Fortunately, the truism that “there is no problem in software which can't be solved by adding yet another level of indirection” applies here. To elevate metafunctions to the status of first-class objects, the MPL introduces the concept of a “metafunction class”:

This form should look familiar to anyone acquainted with function objects in STL, with the nested apply template taking the same role as the runtime function-call operator. In fact, compile-time metafunction classes have the same relationship to metafunctions that runtime function objects have to functions:

2.2.4. One size fits all?

The metafunction class form solves all the problems with ordinary template metafunction mentioned earlier: since it is a regular class, it can be placed in compile-time metadata sequences and manipulated by other metafunctions using the same protocols as for any other metadata. We thereby avoid the code-duplication needed to provide versions of each library component to operate on ordinary metadata and on metafunctions with each distinct supported arity.

On the other hand, it seems that accepting metafunction classes as the representation for compile-time function entities imposes code duplication danger as well: if the library's own primitives, algorithms, etc. are represented as class templates, that means that one either cannot reuse these algorithms in the context of higher-order functions, or she have to duplicate all algorithms in the second form, so, for instance, there would be two versions of find:

That too would hurt usability, considering that the direct invocations of library's algorithms are far more often-used than passing algorithms as arguments to other algorithms/metafunctions.

2.2.5. From metafunction to metafunction class

The MPL's answer to this dilemma is lambda expressions. Lambda is the mechanism that enables the library to curry metafunctions and convert them into metafunction classes, so when one wants to pass the find algorithm as an argument to a higher-order metafunction, she just write:

where _1 and _2 are placeholders for the first and second arguments to the resulting metafunction class. This preserves the intuitive syntax below for when the user wants to use find directly in her code:

2.3. Sequences, algorithms, and iterators

2.3.1. Introduction

Compile-time iteration over a sequence (of types) is one of the basic concepts of template metaprogramming. Differences in types of objects being manipulated is the most common point of variability of similar but not identical code/design, and such designs are the direct target for some metaprogramming. Templates were originally designed to solve this exact problem (e.g. std::vector). However, without predefined abstractions/constructs for manipulating/iterating over sequences of types (as opposed to standalone types), and without known techniques for emulating these constructs using the current language facilities, their effect on helping high-level metaprogramming happen has been limited.

Czarnecki and Eisenecker [CE98], [CE00] were the first to introduce compile-time sequences of types and some simple algorithms on them, although the idea of representing common data structures like trees, lists, etc. at compile time, using class template composition has been around for a while (e.g. most of the expression template libraries build such trees as a part of their expression "parsing" process [Vel95b]). Alexandrescu [Ale01] used lists of types and some algorithms on them to implement several design patterns; the accompanying code is known as the Loki library [Loki].

2.3.2. Algorithms and sequences

Most of the algorithms in the Boost Metaprogramming Library operate on sequences. For example, searching for a type in a list looks like this:

Here, find accepts two parameters - a sequence to search (types) and the type to search for (long) - and returns an iterator iter pointing to the first element of the sequence such that iter::type is identical to long. If no such element exists, iter is identical to end<types>::type. Basically, this is how one would search for a value in a std::list or std::vector, except that mpl::find accepts the sequence as a single parameter, while std::find takes two iterators. Everything else is pretty much the same - the names are the same, the semantics are very close, there are iterators, and one can search not only by type, but also by using a predicate:

typedef mpl::find_if< types,boost::is_float<_> >::type iter;

This conceptual/syntactical similarity with the STL is not coincidental. Reusing the conceptual framework of the STL in the compile-time world allows us to apply familiar and sound approaches for dealing with sequential data structures. The algorithms and idioms which programmers already know from the STL can be applied again at compile-time. We consider this to be one of MPL's greatest strengths, distinguishing it from earlier attempts to build a template metaprogramming library.

2.3.3. Sequence concepts

In the find example above, we searched for the type in a sequence built using the mpl::list template; but list is not the only sequence that the library provides. Neither is mpl::find or any other algorithm hard-coded to work only with list sequences. list is just one model of MPL's Forward Sequence concept, and find works with anything that satisfies this concept's requirements. The hierarchy of sequence concepts in MPL is quite simple - a Sequence is any compile-time entity for which begin<> and end<> produce iterators to the range of its elements; a Forward Sequence is a Sequence whose iterators satisfy Forward Iterator requirements; a Bidirectional Sequence is a Forward Sequence whose iterators satisfy Bidirectional Iterator requirements; finally, a Random Access Sequence is a Bidirectional Sequence whose iterators satisfy Random Access Iterator requirements. 3

Decoupling algorithms from particular sequence implementations (through iterators) allows a metaprogrammer to create her own sequence types and to retain the rest of the library at her disposal. For example, one can define a tiny_list for dealing with sequences of three types as follows:

While the tiny_list itself might be not that interesting (after all, it can hold only three elements), if the technique above could be automated so we would be able to define not-so-tiny sequences (with five, ten, twenty, etc. elements), it would be very valuable. 4

External code generation is an option, but there exists a solution within the language. However, it is not a template metaprogramming, but rather preprocessor metaprogramming. In fact, MPL's vector - a fixed-size type sequence that provides random-access iterators - is implemented very much like the above tiny_list - using the Boost Preprocessor library [PRE].

2.3.4. Ad hoc example revisited

So, the library provides its users with almost complete compile-time equivalent of the STL framework. Does it help them to solve their metaprogramming tasks? Let's return to our earlier largest example to see if we can rewrite it in a better way with what MPL has to offer. Well, actually, there is not much to look at, because the MPL implementation is a one-liner (we'll spread it out here for readability) 5 :

There are no more termination conditions with tricky pattern matching, no more partial specializations; and even more importantly, it's obvious what the above code does - even although it's all templates - something that one could not say about the original version.

2.3.5. iter_fold as the main iteration algorithm

For the purpose of examining a little bit more of the library's internal structure, let's look at how max_element from the above example is implemented. One might expect that now we will again see all these awkward partial specializations, esoteric pattern matching, etc. Well, let's see:

The first thing to notice here is that this algorithm is implemented in terms of another one: iter_fold. In fact, this is probably the most important point of the example, because nearly all other generic sequence algorithms in the library are implemented in terms of iter_fold. If a user should ever need to implement her own sequence algorithm, she'll almost certainly be able to do so using this primitive, which means she won't have to resort to implementing hand-crafted iteration, pattern matching of special cases for loop termination, or workarounds for lack of partial specialization. It also means that her algorithm will automatically benefit from any optimizations the library has implemented, (e.g. recursion unrolling), and that it will work with any sequence that is a model of ForwardSequence, because iter_fold does not require anything more of its sequence argument.

iter_fold algorithm is basically a compile-time equivalent of the fold or reduce functions that comprise the basic and well-known primitives of many functional programming languages. An analogy more familiar to a C++ programmer would be the std::accumulate algorithm from the C++ standard library ([ISO98], section 26.4.1 [lib.accumulate]). However, iter_fold is designed to take advantage of the natural characteristics of recursive traversal: it accepts two metafunction class arguments, the first of which is applied to the state "on the way in" and the second of which is applied "on the way out".

The algorithm “returns” the result of two-way successive applications of binary ForwardOp and BackwardOp operations to iterators in range [begin<Sequence>::type, end<Sequence>::type) and previous result of an operation; the InitialState is logically placed before the sequence and included in the forward traversal. The result type is identical to InitialState if the sequence is empty.

The library also provides iter_fold_backward, fold, and fold_backward algorithms which wrap iter_fold to accommodate its most common usage patterns.

2.3.6. Sequences of numbers

What we've seen so far were sequences (and algorithms on sequences) of types. It is both possible and easy to manipulate compile-time values using the library as well. The only thing to remember is that in C++, class template non-type template parameters give us one more example of non-polymorphic behavior. In other words, if one declared a metafunction to take a non-type template parameter (e.g. long) it's not possible to pass anything besides compile-time integral constants to it:

While this may be an obvious limitation, it imposes yet another dilemma on the library design: on the one hand, we don't want to restrict users to type manipulations only, and on the other hand, full support for integral manipulations would require at least duplication of most of the library facilities 6 - the same situation as we would have if we had chosen to represent metafunctions as ordinary class templates. The solution for this issue is the same as well: we represent integral values by wrapping them in types 7 . For example, to create a list of numbers one can write:

Wrapping integral constants into types to make them first-class citizens is important well inside metaprograms, where one often doesn't know (and doesn't care) if the metafunctions she is using operate on types, integral values, other metafunctions, or something else, like fixed-point or rational numbers (mpl::fixed_c and mpl::rational_c).

But, from the user's perspective, the above example is much more verbose than the shorter, incorrect one. Thus, for the purpose of convenience, the library does provide users with a template that takes non-type template parameters, but offers a more compact notation:

typedef mpl::list_c<long,1,2,3,4,5> numbers;

There is a similar vector counterpart as well:

typedef mpl::vector_c<long,1,2,3,4,5> numbers;

2.3.7. A variety of sequences

Previous efforts to provide generalized metaprogramming facilities for C++ have always concentrated on cons-style type lists and a few core algorithms like size and at, which are tied to the specific sequence implementation. Such systems have an elegant simplicity reminiscent of the analogous functionality in pure functional Lisp. It is much more time-consuming to implement even a basic set of the sequence algorithms provided by equivalent run-time libraries (the STL in particular), but if we have learned anything from the STL, it is that tying those algorithms' implementations to a specific sequence implementation is a misguided effort!

The truth is that there is no single “best” type sequence implementation for the same reasons that there will never be a single “best” runtime sequence implementation. Furthermore, there are already quite a number of type list implementations in use today; and just as the STL algorithms can operate on sequences which don't come from STL containers, so the MPL algorithms are designed to work with foreign type sequences.

It may be an eye-opening fact for some that type lists are not the only useful compile-time sequence. Again, the need for a variety of compile-time containers arises for the same reasons that we have lists, vectors, deques, and sets in the C++ standard library - different containers have different functional and performance characteristics which determine not only applicability and efficiency of particular algorithms, but also the expressiveness or verbosity of the code that uses them. While runtime performance is not an issue for C++ metaprograms, compilation speed is often a significant bottleneck to advanced C++ software development [Abr01].

The MPL provides five built-in sequences: list, list_c (really just a list of value wrappers), vector, a randomly-accessible sequence of fixed maximum size, vector_c, and range_c, a randomly-accessible sequence of consecutive integral values. More important, however, is its ability to adapt to arbitrary sequence types. The only core operations that a sequence is required to provide in order to be used with the library algorithms are begin<> and end<> metafunctions which "return" iterators into the sequence. As with the STL, it is the iterators which are used to implement most of the general-purpose sequence algorithms the library provides. Also, as with the STL, algorithm specialization is used to take advantage of implementation knowledge about particular sequences: many of the "basic" sequence operations such as back<>, front<>, size<>, and at<> are specialized on sequence type to provide a more efficient implementation than the fully generic version.

2.3.8. Loop/recursion unrolling

Almost coincidentally, loop unrolling can be as important to compile-time iterative algorithms as it is to runtime algorithms. To see why, one must first remember that all "loops" in C++ metaprograms, are in fact, implemented with recursion, and that the template instantiation depth can be a valuable resource in a compiler implementation. In fact, Annex B of the C++ standard ([ISO98], annex B [limits]) recommends a minimum depth of 17 recursively nested template instantiations; but this is far too low for many serious metaprograms, some of which easily exceed the hard-coded instantiation limits of some otherwise excellent compilers. To see how this works in action, let's examine a straightforward implementation of the fold metafunction, which combines some algorithm state with each element of a sequence:

Although simple and elegant, this implementation will always incur at least as many levels of recursive template instantiation as there are elements in the input sequence. 8 The library addresses this problem by explicitly "unrolling" the recursion. To apply the technique to our fold example, we begin by factoring out a single step of the algorithm. Our fold_impl_step metafunction has two results: type (the next state), and iterator (the next sequence position).

The MPL applies this unrolling technique across all algorithms with an unrolling factor tuned according to the demands of the C++ implementation in use, and with an option for the user to override the value. 9 This fact enables users to push beyond the metaprogramming limits they would usually encounter with more naive algorithm implementations. Experiments also show a small (up to 10%) increase in metaprogram instantiation speed on some compilers when loop unrolling is used.

3. Lambda facility

The MPL's lambda facility allows the inline composition of class templates into “lambda expressions”, which are classes and can therefore be passed around as ordinary metafunction classes, or transformed into metafunction classes before application using the expression:

typedef mpl::lambda<expr>::type func;

For example, boost::remove_const traits template from Boost type_traits library [TTL] is a class template (obviously), or a metafunction in MPL terminology. The simplest example of an “inline composition” of it would be something like:

typedef boost::remove_const<_1> expr;

This forms a so called “lambda expression”, which is neither a metafunction class, nor a metafunction, yet can be passed around everywhere because it's an ordinary C++ class, because all MPL facilities are polymorphic with respect to their arguments. Now, that lambda expression can be transformed into a metafunction class using the MPL's lambda facility:

And one does not have to specify the last part (typedef lambda<expr>::type func), because all the algorithms do this to any of their metafunction class operands internally (a lambda<T>::type expression applied to a metafunction class gives back the same metafunction class, so it's safe to apply the expression unconditionally).

The alternative way to write an equivalent of the above metafunction class would be:

Or to use mpl::compose_ family of templates in a similar way. Here, we use mpl::meta_fun templates to convert metafunctions into metafunction classes and then combine them using mpl::bind. The transformation from this form to the above inline lambda expression and vice-versa is mechanical, and that is essentially what the typedef mpl::lambda<expr>::type expression does.

For its own metafunctions (algorithms, primitives, etc.), MPL enables one to write the above in a less cumbersome way:

4. Code generation facilities

There are cases, especially in the domain of numeric computation, when one wants to perform some part of the calculations at compile-time, and then pass the results to a run-time part of the program for further processing. For example, suppose one has implemented a complex compile-time algorithm that works with fixed-point arithmetic:

Suppose the result_data here is a sequence of mpl::fixed_c types that keeps the results of the algorithm, and now one wishes to feed that result to the run-time part of the algorithm. With MPL she can do this:

To call the function, one is required to explicitly provide two actual template parameters, a compile-time sequence Seq and a unary transformation metafunction TransformOp, plus a run-time function argument f (in our example, numbers, _, and boost::bind(...) correspondingly). f is a function object which operator() is called for every element in the Seq tranfromed by TransformOp.

5. Example: a compile-time FSM generator

Finite state machines (FSMs) are an important tool for describing and implementing program behavior [HU79], [Mar98]. They also are a good example of a domain in which metaprogramming can be applied to reduce the amount of repetitive and boilerplate operations one must perform in order to implement these simple mathematical models in code. Below we present a simple state machine generator that has been implemented using Boost Metaprogramming Library facilities. The generator takes a compile-time automata description, and converts it into C++ code that implements the FSM at run-time.

The FSM description is basically a combination of states and events plus a state transition table (STT), which ties them all together. The generator walks through the table and generates the state machine's process_event method that is the essence of an FSM.

Suppose we want to implement a simple music player using a finite state machine model. The state transition table for the FSM is shown in Table 1. The STT format reflects the way one usually describes the behavior of an FSM in plain English. For example, the first line of the table can be read as follows: “If the model is in the stopped state and the play_event is received, then the do_play transition function is called, and the model transitions to the playing state”.

State

Event

Next state

Transition function

stopped

play_event

playing

do_play

playing

stop_event

stopped

do_stop

playing

pause_event

paused

do_pause

paused

play_event

playing

do_resume

paused

stop_event

stopped

do_stop

Table 1. Player's state transition table with actions

The transition table provides us with a complete formal definition of the target FSM, and there are several ways to transform that definition into code. For instance, if we define states as members of an enumeration type, and events as classes derived from some base event class 10 , like so:

Although there is nothing particularly wrong with implementing an FSM's structure using nested if (or switch-case) statements, the obvious weakness of this approach is that most of the above code is boilerplate. What one tends to do with boilerplate code is to copy and paste it, then change names etc. to adjust it to its new location; and that's where the errors are most likely to creep in. Since all the lines of event processing look alike (structurally), it's very easy to overlook or forget something that needs to be changed, and many such errors won't appear until the runtime.

The transition table of our FSM is just five lines long; ideally, we would like the skeleton implementation of the automata's controlling logic to be equally short (or, at least, to look equally short, i.e. to be encapsulated in some form so we never worry about it).

5.1. Implementation

To represent the STT in a C++ program, we define a transition class template that represents a single line of the table. Then the table itself can be represented as a sequence of such lines:

That's all - the above will generate a complete FSM implementation according to our specification. The only thing we need before using it is the definition of the event types (that were just forward declared before):

5.2. Related work

A notable prior work in the field of automation of general-purpose state machine implementation in C++ is the Robert Martin's State Machine Compiler[SMC]. The SMC takes an ASCII description of the machine's state transition table and produces C++ code that implements the FSM using a variation of State design pattern [Hun91], [GHJ+95]. Lafreniere [Laf00] presents another approach, where no external tools are used, and the FSMs are table driven.

6. Acknowledgements

Peter Dimov contributed the bind functionality without which compile-time lambda expressions wouldn't have been possible. The MPL implementation would have been much more difficult without Vesa Karvonen's wonderful Boost Preprocessor Metaprogramming Library. Authors are also greatly indebted to David B. Held who kindly volunteered to thoroughly edit this document. Of course, any remaining errors are exclusively ours.

1 Although it would be easy to implement pointed_type using partial specialization to distinguish the case where T is a pointer, if_ is likely to be the right tool for dealing with more complex conditionals. For the purposes of exposition, please suspend disbelief!

2 In fact it's already broken: apply_twice doesn't even fit the metafunction concept since it requires a template (rather than a type) as its first parameter, which breaks the metafunction protocol.

3 A more precise definition of these concepts can be found in the library reference documentation [MPLR].

4 Random access is almost as important at compile-time as it is at run-time. For example, searching for an item in a sorted random-access sequence using lower_bound can be much faster than performing the same operation on a forward-access-only list.